Search Results (59)

This study investigates whether specific acoustic features can reliably indicate phrase boundaries for automatic detection. It includes (1) an analysis of acoustic markers at the end of prosodic units—intonational phrases, intermediate phrases, and words—and (2) the evaluation of these features in an automatic boundary detection algorithm. Data were drawn from professionally and expressively read speech (893 words), news broadcasts (732 words), and interviews (361 words). Key features analyzed were pause duration, final sound lengthening, intensity, and F0 changes. Findings show that pauses and their duration are the most consistent indicators of phrase boundaries, especially at intonational phrase ends. Final sound lengthening and reductions in intensity and F0 also contribute but are less reliable for intermediate phrases. In automatic detection phonetic cues can be used to predict boundaries assigned by phoneticians 69% of the time. Read speech yielded better results than spontaneous speech. Among the features, pause presence and length were the most reliable, while F0 and intensity changes played a minor role. Full article

The risk of developing symptomatic knee osteoarthritis (KOA) during a lifetime, i.e., pain, aching, or stiffness in a joint associated with radiographic KOA, was estimated in 2008 to be around 40% in men and 47% in women. The clinical and scientific communities lack an efficient diagnostic method to effectively monitor, evaluate, and predict the evolution of KOA before and during the therapeutic protocol. In this review, we summarize the main methods that are used or seem promising for the diagnosis of osteoarthritis, with a specific focus on non- or low-invasive methods. As standard diagnostic tools, arthroscopy, magnetic resonance imaging (MRI), and X-ray radiography provide spatial and direct visualization of the joint. However, discrepancies between findings and patient feelings often occur, indicating a lack of correlation between current imaging methods and clinical symptoms. Alternative strategies are in development, including the analysis of biochemical markers or acoustic emission recordings. These methods have undergone deep development and propose, with non- or minimally invasive procedures, to obtain data on tissue condition. However, they present some drawbacks, such as possible interference or the lack of direct visualization of the tissue. Other original methods show strong potential in the field of KOA monitoring, such as electrical bioimpedance or near-infrared spectrometry. These methods could permit us to obtain cheap, portable, and non-invasive data on joint tissue health, while they still need strong implementation to be validated. Also, the use of Artificial Intelligence (AI) in the diagnosis seems essential to effectively develop and validate predictive models for KOA evolution, provided that a large and robust database is available. This would offer a powerful tool for researchers and clinicians to improve therapeutic strategies while permitting an anticipated adaptation of the clinical protocols, moving toward reliable and personalized medicine. Full article

Diagnosing Parkinson’s disease (PD) through speech analysis is a promising area of research, as speech impairments are often one of the early signs of the disease. This study investigates the efficacy of fine-tuning pre-trained Automatic Speech Recognition (ASR) models, specifically Wav2Vec 2.0 and HuBERT, for PD detection using transfer learning. These models, pre-trained on large unlabeled datasets, can be capable of learning rich speech representations that capture acoustic markers of PD. The study also proposes the integration of a supervised contrastive (SupCon) learning approach to enhance the models’ ability to distinguish PD-specific features. Additionally, the proposed ASR-based features were compared against two common acoustic feature sets: mel-frequency cepstral coefficients (MFCCs) and the extended Geneva minimalistic acoustic parameter set (eGeMAPS) as a baseline. We also employed a gradient-based method, Grad-CAM, to visualize important speech regions contributing to the models’ predictions. The experiments, conducted using the NeuroVoz dataset, demonstrated that features extracted from the pre-trained ASR models exhibited superior performance compared to the baseline features. The results also reveal that the method integrating SupCon consistently outperforms traditional cross-entropy (CE)-based models. Wav2Vec 2.0 and HuBERT with SupCon achieved the highest F1 scores of 90.0% and 88.99%, respectively. Additionally, their AUC scores in the ROC analysis surpassed those of the CE models, which had comparatively lower AUCs, ranging from 0.84 to 0.89. These results highlight the potential of ASR-based models as scalable, non-invasive tools for diagnosing and monitoring PD, offering a promising avenue for the early detection and management of this debilitating condition. Full article

Background and Objectives: Aortic stenosis (AS) leads to progressive left ventricular (LV) dysfunction, making early detection crucial. Global longitudinal strain (GLS) is an echocardiographic marker of subclinical LV dysfunction; however, echocardiography has limitations, including operator dependency and acoustic variability. Cardiac magnetic resonance (CMR) is a valuable complementary tool, and artificial intelligence (AI) may enhance strain measurement accuracy, though its role in AS remains underexplored. To evaluate the performance of an AI-based CMR feature tracking tool for the assessment of LV global and segmental GLS in AS patients and compare results with the respective measurements from healthy volunteers (control group), as well as with the GLS obtained using the echocardiographic speckle tracking technique. Materials and Methods: This retrospective study analysed 111 CMR exams (70 AS patients, 41 healthy controls) from a single centre. AI-derived GLS values from gradient echo 2-, 3-, and 4-chamber CMR views were manually reviewed for accuracy. Error rates, segmental, and global myocardial strain differences were assessed between AS patients and the control group. Results: AI-based CMR GLS strongly correlated with echocardiographic GLS (r = 0.694, p < 0.001) and showed lower variability. The AI-derived GLS from CMR was significantly lower in aortic stenosis patients compared to controls (−17.86 ± 3.47 vs. −20.70 ± 1.98). However, AI-based strain analysis had an overall error rate of 6%, which was significantly higher in AS patients (18.6%) compared to healthy controls (2.44%) (p = 0.0088). The 3-chamber CMR view was the most error-prone (50% of isolated errors). Segmental strain variability between AS patients and controls was most pronounced in basal segments, with smaller differences in middle and apical segments. CMR demonstrated greater precision than echocardiography, as indicated by a smaller standard deviation in GLS measurements (3.47 vs. 4.98). Conclusions: The AI-based CMR feature tracking technique provides accurate and reproducible GLS measurements, showing strong agreement with echocardiographic speckle tracking-based GLS. However, the higher error rates in AS patients compared to controls underscore the need for more advanced AI algorithms to improve performance in cardiac pathology. Full article

Left ventricular assist device (LVAD) thrombosis typically presents late and may have devastating consequences for patients. While LVAD pump thrombosis is uncommon with current pump designs, many patients worldwide remain supported with previous generations of LVADs, including the HeartWare device (HVAD). Researchers have focused on investigating the acoustic signatures of LVADs to enable earlier detection and treatment of this condition. This study explored the use of machine learning algorithms to predict thrombosis from harmonic power values determined from the acoustic signatures of a cohort of HVAD patients (n = 11). The current dataset was too small to develop a predictive model for new data, but exhaustive cross validation indicated that machine learning models using the first two or the first three harmonic power values both resulted in reasonable prediction accuracy of the thrombosis outcome. Furthermore, when principal component analysis (PCA) was applied to the harmonic power variables from these promising models, the use of the resulting PCA variables in machine learning models further increased the thrombosis outcome prediction accuracy. K-nearest neighbor (KNN) models gave the best predictive accuracy for this dataset. Future work with a larger HVAD recording dataset is necessary to develop a truly predictive model of HVAD thrombosis. Such a predictive model would provide clinicians with a marker to detect HVAD thrombosis based directly on pump performance, to be used along with current clinical markers. Full article

This study explores rhythm metrics as a sociolinguistic marker in Philippine English (PhE), addressing gaps in understanding rhythmic variation in Southeast Asian Englishes. It aims to uncover how rhythmic patterns reflect sociolectal identities within a multilingual context. Using acoustic data from 30 participants in Manila, rhythm metrics (%V, ΔV, ΔC, nPVI, and rPVI) were analyzed to examine rhythmic tendencies. The findings reveal distinct patterns: PhE acrolect aligns with stress-timed rhythms of general American English, PhE basilect reflects syllable-timed features similar to Spanish, and PhE mesolect occupies a hybrid position blending elements of both. By emphasizing rhythm as a key identifier of sociolectal variation, this study advances the understanding of linguistic diversity in World Englishes and provides a novel framework for exploring identity in multilingual settings. Full article

Background/Objectives: Voice analysis has shown promise in anxiety assessment, yet traditional approaches examining isolated acoustic features yield inconsistent results. This study aimed to explore the relationship between anxiety states and vocal parameters from a network perspective in ecologically valid settings. Methods: A cross-sectional study was conducted with 316 undergraduate students (191 males, 125 females; mean age 20.3 ± 0.85 years) who completed a standardized picture description task while their speech was recorded. Participants were categorized into low-anxiety (n = 119) and high-anxiety (n = 197) groups based on self-reported anxiety ratings. Five acoustic parameters—jitter, fundamental frequency (F0), formant frequencies (F1/F2), intensity, and speech rate—were analyzed using network analysis. Results: Network analysis revealed a robust negative relationship between jitter and state anxiety, with jitter as the sole speech parameter consistently linked to state anxiety in the total group. Additionally, higher anxiety levels were associated with a coupling between intensity and F1/F2, whereas the low-anxiety network displayed a sparser organization without intensity and F1/F2 connection. Conclusions: Anxiety could be recognized by speech parameter networks in ecological settings. The distinct pattern with the negative jitter-anxiety relationship in the total network and the connection between intensity and F1/2 in high-anxiety states suggest potential speech markers for anxiety assessment. These findings suggest that state anxiety may directly influence jitter and fundamentally restructure the relationships among speech features, highlighting the importance of examining jitter and speech parameter interactions rather than isolated values in speech detection of anxiety. Full article

The accurate diagnosis of adnexal masses is a critical challenge in gynecological practice. Current ultrasound-based models, such as the ADNEX model, IOTA Simple Rules, and O-RADS, have demonstrated good diagnostic performance but are limited by the inclusion of demographic factors and solid confounding lesions. This study aimed to develop and validate a novel ultrasound score (ECO-SCORE) for cystic and solid-cystic lesions based solely on imaging characteristics to improve diagnostic accuracy and applicability in clinical practice. Methods: We conducted a retrospective study on 330 women diagnosed with adnexal masses, including 251 benign and 79 malignant cases. Ultrasound features were analyzed using logistic regression to identify key predictors of malignancy. A new scoring model was developed, excluding demographic or tumor-marker data. Diagnostic performance metrics, including sensitivity, specificity, AUC, and odds ratios, were calculated and compared to existing models using a testing set (20% of the data). Results: The ECO-SCORE achieved an AUC of 97.08%, outperforming ADNEX model (87.5%), IOTA Simple Rules (85.7%), and O-RADS (87.5%). Sensitivity and specificity were 92.98% and 88.88%, respectively, with an odds ratio of 106. Key predictors included irregular contour, absence of acoustic shadows, vascularization within solid areas, and vascularization of papillae. Conclusions: The ECO-SCORE demonstrated superior diagnostic accuracy compared to established models, highlighting its potential as a reliable tool for assessing adnexal masses using ultrasound features exclusively. Further multicenter validation is needed to confirm its robustness across different clinical settings. Full article

Autonomous Underwater Vehicles (AUVs) operate independently using onboard batteries and data storage, necessitating periodic recovery for battery recharging and data transfer. Traditional surface-based launch and recovery (L&R) operations pose significant risks to personnel and equipment, particularly in adverse weather conditions. Subsurface docking stations provide a safer alternative but often involve complex fixed installations and costly acoustic positioning systems. This work introduces a comprehensive docking solution featuring the following two key innovations: (1) a novel deployable docking station (DDS) designed for rapid deployment from vessels of opportunity, operating without active acoustic transmitters; and (2) an innovative sensor fusion approach that combines the AUV’s onboard forward-looking sonar and camera data. The DDS comprises a semi-submersible protective frame and a subsurface, heave-compensated docking component equipped with backlit visual markers, an electromagnetic (EM) beacon, and an EM lifting device. This adaptable design is suitable for temporary installations and in acoustically sensitive or covert operations. The positioning and guidance system employs a multi-sensor approach, integrating range and azimuth data from the sonar with elevation data from the vision camera to achieve precise 3D positioning and robust navigation in varying underwater conditions. This paper details the design considerations and integration of the AUV system and the docking station, highlighting their innovative features. The proposed method was validated through software-in-the-loop simulations, controlled seawater pool experiments, and preliminary open-sea trials, including several docking attempts. While further sea trials are planned, current results demonstrate the potential of this solution to enhance AUV operational capabilities in challenging underwater environments while reducing deployment complexity and operational costs. Full article

Prostate-specific antigen (PSA) is a well-established tumour marker for prostatic carcinoma. In this study, we present a novel, real-time, and ultrasensitive Love-mode surface acoustic wave (L-SAW) immunosensor for PSA detection enhanced by MoS₂@Cu₂O-Au nanocomposite conjugation. The MoS₂@Cu₂O-Au nanocomposites were analyzed by SEM, XRD, and EDS. The experiments show a significant improvement in sensitivity and detection limit compared with the previous detection methods utilizing nanogold alone to detect PSA biomolecules. The experimental results show a good linear relationship when the range of PSA concentrations between 200 pg/mL and 5 ng/mL was tested. The experimental results also show good specificity against alpha 1 fetoprotein and L-tryptophan disruptors. Full article

The integrity of the blood–labyrinth barrier (BLB) is essential for inner ear homeostasis, regulating the ionic composition of endolymph and perilymph and preventing harmful substance entry. Endothelial hyperpermeability, central in inflammatory and immune responses, is managed through complex intercellular communication and molecular signaling pathways. Recent studies link BLB permeability dysregulation to auditory pathologies like acoustic trauma, autoimmune inner ear diseases, and presbycusis. Polymorphonuclear granulocytes (PMNs), or neutrophils, significantly modulate vascular permeability, impacting endothelial barrier properties. Neutrophil extracellular traps (NETs) are involved in diseases with autoimmune and autoinflammatory bases. The present study evaluated the impact of NETs on a BLB cellular model using a Transwell^® setup. Our findings revealed a concentration-dependent impact of NETs on human inner ear-derived endothelial cells. In particular, endothelial permeability markers increased, as indicated by reduced transepithelial electrical resistance, enhanced dextran permeability, and downregulated junctional gene expression (ZO1, OCL, and CDH5). Changes in cytoskeletal architecture were also observed. These preliminary results pave the way for further research into the potential involvement of NETs in BLB impairment and implications for auditory disorders. Full article

Background: Bipolar disorder (BD) involves significant mood and energy shifts reflected in speech patterns. Detecting these patterns is crucial for diagnosis and monitoring, currently assessed subjectively. Advances in natural language processing offer opportunities to objectively analyze them. Aims: To (i) correlate speech features with manic-depressive symptom severity in BD, (ii) develop predictive models for diagnostic and treatment outcomes, and (iii) determine the most relevant speech features and tasks for these analyses. Methods: This naturalistic, observational study involved longitudinal audio recordings of BD patients at euthymia, during acute manic/depressive phases, and after-response. Patients participated in clinical evaluations, cognitive tasks, standard text readings, and storytelling. After automatic diarization and transcription, speech features, including acoustics, content, formal aspects, and emotionality, will be extracted. Statistical analyses will (i) correlate speech features with clinical scales, (ii) use lasso logistic regression to develop predictive models, and (iii) identify relevant speech features. Results: Audio recordings from 76 patients (24 manic, 21 depressed, 31 euthymic) were collected. The mean age was 46.0 ± 14.4 years, with 63.2% female. The mean YMRS score for manic patients was 22.9 ± 7.1, reducing to 5.3 ± 5.3 post-response. Depressed patients had a mean HDRS-17 score of 17.1 ± 4.4, decreasing to 3.3 ± 2.8 post-response. Euthymic patients had mean YMRS and HDRS-17 scores of 0.97 ± 1.4 and 3.9 ± 2.9, respectively. Following data pre-processing, including noise reduction and feature extraction, comprehensive statistical analyses will be conducted to explore correlations and develop predictive models. Conclusions: Automated speech analysis in BD could provide objective markers for psychopathological alterations, improving diagnosis, monitoring, and response prediction. This technology could identify subtle alterations, signaling early signs of relapse. Establishing standardized protocols is crucial for creating a global speech cohort, fostering collaboration, and advancing BD understanding. Full article

Post-acute COVID-19 vaccination syndrome (PACVS) is a chronic disease triggered by SARS-CoV-2 vaccination (estimated prevalence 0.02%). PACVS is discriminated from the normal post-vaccination state by altered receptor antibodies, most notably angiotensin II type 1 and alpha-2B adrenergic receptor antibodies. Here, we investigate the clinical phenotype using a study registry encompassing 191 PACVS-affected persons (159 females/32 males; median ages: 39/42 years). Unbiased clustering (modified Jaccard index) of reported symptoms revealed a prevalent cross-cohort symptomatology of malaise and chronic fatigue (>80% of cases). Overlapping clusters of (i) peripheral nerve dysfunction, dysesthesia, motor weakness, pain, and vasomotor dysfunction; (ii) cardiovascular impairment; and (iii) cognitive impairment, headache, and visual and acoustic dysfunctions were also frequently represented. Notable abnormalities of standard serum markers encompassing increased interleukins 6 and 8 (>80%), low free tri-iodine thyroxine (>80%), IgG subclass imbalances (>50%), impaired iron storage (>50%), and increased soluble neurofilament light chains (>30%) were not associated with specific symptoms. Based on these data, 131/191 participants fit myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) and simultaneously also several other established dysautonomia syndromes. Furthermore, 31/191 participants fit none of these syndromes. In conclusion, PACVS could either be an outlier of ME/CFS or a dysautonomia syndrome sui generis. Full article

Brain imaging studies have recently provided some evidence in favor of covert cognitive processes that are ongoing in patients with disorders of consciousness (DoC) (e.g., a minimally conscious state and vegetative state/unresponsive wakefulness syndrome) when engaged in passive sensory stimulation or active tasks such as motor imagery. In this exploratory study, we used transcranial magnetic stimulation (TMS) of the motor cortex to assess modulations of corticospinal excitability induced by action observation in eleven patients with DoC. Action observation is known to facilitate corticospinal excitability in healthy subjects, unveiling how the observer’s motor system maps others’ actions onto her/his motor repertoire. Additional stimuli were non-biological motion and acoustic startle stimuli, considering that sudden and loud acoustic stimulation is known to lower corticospinal excitability in healthy subjects. The results indicate that some form of motor resonance is spared in a subset of patients with DoC, with some significant difference between biological and non-biological motion stimuli. However, there was no covariation between corticospinal excitability and the type of DoC diagnosis (i.e., whether diagnosed with VS/UWS or MCS). Similarly, no covariation was detected with clinical changes between admission and discharge in clinical outcome measures. Both motor resonance and the difference between the resonance with biological/non-biological motion discrimination correlated with the amplitude of the N20 somatosensory evoked potentials, following the stimulation of the median nerve at the wrist (i.e., the temporal marker signaling the activation of the contralateral primary somatosensory cortex). Moreover, the startle-evoking stimulus produced an anomalous increase in corticospinal excitability, suggesting a functional dissociation between cortical and subcortical circuits in patients with DoC. Further work is needed to better comprehend the conditions in which corticospinal facilitation occurs and whether and how they may relate to individual clinical parameters. Full article

Optimising drug delivery to tumours remains an obstacle to effective cancer treatment. A prerequisite for successful chemotherapy is that the drugs reach all tumour cells. The vascular network of tumours, extravasation across the capillary wall and penetration throughout the extracellular matrix limit the delivery of drugs. Ultrasound combined with microbubbles has been shown to improve the therapeutic response in preclinical and clinical studies. Most studies apply microbubbles designed as ultrasound contrast agents. Acoustic Cluster Therapy (ACT^®) is a novel approach based on ultrasound-activated microbubbles, which have a diameter 5–10 times larger than regular contrast agent microbubbles. An advantage of using such large microbubbles is that they are in contact with a larger part of the capillary wall, and the oscillating microbubbles exert more effective biomechanical effects on the vessel wall. In accordance with this, ACT^® has shown promising therapeutic results in combination with various drugs and drug-loaded nanoparticles. Knowledge of the mechanism and behaviour of drugs and microbubbles is needed to optimise ACT^®. Real-time intravital microscopy (IVM) is a useful tool for such studies. This paper presents the experimental setup design for visualising ACT^® microbubbles within the vasculature of tumours implanted in dorsal window (DW) chambers. It presents ultrasound setups, the integration and alignment of the ultrasound field with the optical system in live animal experiments, and the methodologies for visualisation and analysing the recordings. Dextran was used as a fluorescent marker to visualise the blood vessels and to trace drug extravasation and penetration into the extracellular matrix. The results reveal that the experimental setup successfully recorded the kinetics of extravasation and penetration distances into the extracellular matrix, offering a deeper understanding of ACT’s mechanisms and potential in localised drug delivery. Full article